Overview

Dataset statistics

Number of variables22
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory177.3 B

Variable types

Numeric12
Categorical10

Alerts

city has constant value "Lahore" Constant
province_name has constant value "Punjab" Constant
purpose has constant value "For Sale" Constant
page_url has a high cardinality: 95 distinct values High cardinality
price is highly correlated with area_marla and 1 other fieldsHigh correlation
longitude is highly correlated with dayHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
day is highly correlated with longitudeHigh correlation
location_id is highly correlated with longitude and 1 other fieldsHigh correlation
price is highly correlated with area_marla and 1 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
longitude is highly correlated with location_id and 2 other fieldsHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
year is highly correlated with monthHigh correlation
month is highly correlated with yearHigh correlation
day is highly correlated with location_id and 1 other fieldsHigh correlation
price is highly correlated with area_marla and 1 other fieldsHigh correlation
longitude is highly correlated with dayHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
day is highly correlated with longitudeHigh correlation
price_bin is highly correlated with city and 6 other fieldsHigh correlation
city is highly correlated with price_bin and 8 other fieldsHigh correlation
page_url is highly correlated with price_bin and 8 other fieldsHigh correlation
province_name is highly correlated with price_bin and 8 other fieldsHigh correlation
locality is highly correlated with price_bin and 8 other fieldsHigh correlation
property_type is highly correlated with city and 6 other fieldsHigh correlation
location is highly correlated with price_bin and 8 other fieldsHigh correlation
purpose is highly correlated with price_bin and 8 other fieldsHigh correlation
date_added is highly correlated with city and 5 other fieldsHigh correlation
area is highly correlated with price_bin and 7 other fieldsHigh correlation
property_id is highly correlated with location_id and 7 other fieldsHigh correlation
location_id is highly correlated with property_id and 11 other fieldsHigh correlation
page_url is highly correlated with property_id and 16 other fieldsHigh correlation
property_type is highly correlated with page_url and 4 other fieldsHigh correlation
price is highly correlated with page_url and 8 other fieldsHigh correlation
price_bin is highly correlated with location_id and 8 other fieldsHigh correlation
location is highly correlated with property_id and 16 other fieldsHigh correlation
locality is highly correlated with property_id and 16 other fieldsHigh correlation
latitude is highly correlated with location_id and 8 other fieldsHigh correlation
longitude is highly correlated with property_id and 13 other fieldsHigh correlation
baths is highly correlated with location_id and 12 other fieldsHigh correlation
area is highly correlated with property_id and 16 other fieldsHigh correlation
area_marla is highly correlated with page_url and 8 other fieldsHigh correlation
area_sqft is highly correlated with page_url and 8 other fieldsHigh correlation
bedrooms is highly correlated with page_url and 10 other fieldsHigh correlation
date_added is highly correlated with property_id and 15 other fieldsHigh correlation
month is highly correlated with location_id and 8 other fieldsHigh correlation
day is highly correlated with property_id and 10 other fieldsHigh correlation
page_url is uniformly distributed Uniform
property_id has unique values Unique
page_url has unique values Unique
baths has 16 (16.8%) zeros Zeros
bedrooms has 12 (12.6%) zeros Zeros

Reproduction

Analysis started2022-11-16 14:30:39.487186
Analysis finished2022-11-16 14:31:15.933874
Duration36.45 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

property_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3371864.116
Minimum347795
Maximum5019310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:16.053932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum347795
5-th percentile983065.7
Q12471475
median3931998
Q34323660
95-th percentile4698194.6
Maximum5019310
Range4671515
Interquartile range (IQR)1852185

Descriptive statistics

Standard deviation1176444.798
Coefficient of variation (CV)0.3489004176
Kurtosis-0.2625312105
Mean3371864.116
Median Absolute Deviation (MAD)625801
Skewness-0.8067837016
Sum320327091
Variance1.384022363 × 1012
MonotonicityStrictly increasing
2022-11-16T19:31:16.373837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477951
 
1.1%
40171131
 
1.1%
43049771
 
1.1%
42495711
 
1.1%
41705521
 
1.1%
41598261
 
1.1%
41539881
 
1.1%
41116941
 
1.1%
41077151
 
1.1%
41003821
 
1.1%
Other values (85)85
89.5%
ValueCountFrequency (%)
3477951
1.1%
4828921
1.1%
5559621
1.1%
7852891
1.1%
9830651
1.1%
9830661
1.1%
12866431
1.1%
14027841
1.1%
16067101
1.1%
16468801
1.1%
ValueCountFrequency (%)
50193101
1.1%
49514511
1.1%
49406291
1.1%
49075681
1.1%
47474131
1.1%
46771011
1.1%
45709881
1.1%
45609111
1.1%
45577991
1.1%
45368541
1.1%

location_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.294737
Minimum7
Maximum10542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:16.544895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile73.2
Q11604
median3847
Q39433
95-th percentile9436
Maximum10542
Range10535
Interquartile range (IQR)7829

Descriptive statistics

Standard deviation3727.385516
Coefficient of variation (CV)0.7272529108
Kurtosis-1.646148423
Mean5125.294737
Median Absolute Deviation (MAD)3474
Skewness0.09953995338
Sum486903
Variance13893402.78
MonotonicityNot monotonic
2022-11-16T19:31:16.713269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94359
 
9.5%
94346
 
6.3%
94336
 
6.3%
81725
 
5.3%
94364
 
4.2%
17843
 
3.2%
105423
 
3.2%
5143
 
3.2%
94323
 
3.2%
38242
 
2.1%
Other values (45)51
53.7%
ValueCountFrequency (%)
71
1.1%
82
2.1%
481
1.1%
691
1.1%
751
1.1%
1542
2.1%
3731
1.1%
3771
1.1%
3781
1.1%
4961
1.1%
ValueCountFrequency (%)
105423
 
3.2%
97471
 
1.1%
94364
4.2%
94359
9.5%
94346
6.3%
94336
6.3%
94323
 
3.2%
84441
 
1.1%
84251
 
1.1%
81725
5.3%

page_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size888.0 B
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
 
1
https://www.zameen.com/Property/dha_defence_defence_raya_1_kanal_house_for_sale_at_facing_golf_park_dha_raya-4017113-8172-1.html
 
1
https://www.zameen.com/Property/dha_defence_dha_phase_4_house_for_sale_in_dha_phase_4-4304977-1446-1.html
 
1
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html
 
1
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html
 
1
Other values (90)
90 

Length

Max length165
Median length126
Mean length124.8526316
Min length88

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)100.0%

Sample

1st rowhttps://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
2nd rowhttps://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.html
3rd rowhttps://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html
4th rowhttps://www.zameen.com/Property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.html
5th rowhttps://www.zameen.com/Property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.html

Common Values

ValueCountFrequency (%)
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html1
 
1.1%
https://www.zameen.com/Property/dha_defence_defence_raya_1_kanal_house_for_sale_at_facing_golf_park_dha_raya-4017113-8172-1.html1
 
1.1%
https://www.zameen.com/Property/dha_defence_dha_phase_4_house_for_sale_in_dha_phase_4-4304977-1446-1.html1
 
1.1%
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html1
 
1.1%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html1
 
1.1%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_house_is_available_for_sale-4159826-3527-1.html1
 
1.1%
https://www.zameen.com/Property/bahria_town_sector_c_bahria_town_tulip_block_10_marla_brand_new_house_in_sector_c_bahria_town_lahore-4153988-1789-1.html1
 
1.1%
https://www.zameen.com/Property/dha_phase_5_dha_phase_5_block_a_1_kanal_beautiful_bungalow_house_for_sale-4111694-1598-1.html1
 
1.1%
https://www.zameen.com/Property/dha_defence_dha_phase_6_1_kanal_brand_new_beautiful_bungalow_for_sale-4107715-1448-1.html1
 
1.1%
https://www.zameen.com/Property/askari_10_askari_10_sector_f_17_marla_corner_brand_new_brig_house_available_for_sale-4100382-10542-1.html1
 
1.1%
Other values (85)85
89.5%

Length

2022-11-16T19:31:16.902568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.zameen.com/property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html1
 
1.1%
https://www.zameen.com/property/askari_10_askari_10_sector_b_one_kanal_house_khalid_designed_main_boulevard_fully_renovated_available_for_sale-2471472-9433-1.html1
 
1.1%
https://www.zameen.com/property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html1
 
1.1%
https://www.zameen.com/property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.html1
 
1.1%
https://www.zameen.com/property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.html1
 
1.1%
https://www.zameen.com/property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.html1
 
1.1%
https://www.zameen.com/property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.html1
 
1.1%
https://www.zameen.com/property/lahore_upper_mall_commercial_old_house_for_sale_upper_mall_lahore_excellent_location-1402784-514-1.html1
 
1.1%
https://www.zameen.com/property/lahore_cavalry_ground_10_marla_house_is_available_for_sale-1606710-69-1.html1
 
1.1%
https://www.zameen.com/property/bahria_town_sector_b_bahria_town_umar_block_double_storey_house_for_sale-1646880-1781-1.html1
 
1.1%
Other values (85)85
89.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

property_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
House
93 
Flat
 
2

Length

Max length5
Median length5
Mean length4.978947368
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowHouse
5th rowHouse

Common Values

ValueCountFrequency (%)
House93
97.9%
Flat2
 
2.1%

Length

2022-11-16T19:31:17.058321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-16T19:31:17.139460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
house93
97.9%
flat2
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78244210.53
Minimum3200000
Maximum1250000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:17.242873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3200000
5-th percentile7190000
Q120750000
median23500000
Q349750000
95-th percentile366000000
Maximum1250000000
Range1246800000
Interquartile range (IQR)29000000

Descriptive statistics

Standard deviation193512583.4
Coefficient of variation (CV)2.473187244
Kurtosis25.98639693
Mean78244210.53
Median Absolute Deviation (MAD)11500000
Skewness4.927041099
Sum7433200000
Variance3.744711994 × 1016
MonotonicityNot monotonic
2022-11-16T19:31:17.423155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225000005
 
5.3%
230000005
 
5.3%
400000004
 
4.2%
235000004
 
4.2%
215000004
 
4.2%
350000003
 
3.2%
500000003
 
3.2%
220000003
 
3.2%
210000002
 
2.1%
32000002
 
2.1%
Other values (54)60
63.2%
ValueCountFrequency (%)
32000002
2.1%
55000002
2.1%
60000001
1.1%
77000001
1.1%
80000001
1.1%
95000001
1.1%
118000001
1.1%
120000002
2.1%
125000001
1.1%
135000001
1.1%
ValueCountFrequency (%)
12500000001
1.1%
12000000001
1.1%
6200000001
1.1%
4800000001
1.1%
3800000001
1.1%
3600000001
1.1%
2200000001
1.1%
1800000001
1.1%
1600000001
1.1%
875000001
1.1%

price_bin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size888.0 B
Very High
41 
High
40 
Low
Medium

Length

Max length9
Median length4
Mean length6.2
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery High
2nd rowVery High
3rd rowLow
4th rowVery High
5th rowHigh

Common Values

ValueCountFrequency (%)
Very High41
43.2%
High40
42.1%
Low8
 
8.4%
Medium6
 
6.3%

Length

2022-11-16T19:31:17.583224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-16T19:31:17.669173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
high81
59.6%
very41
30.1%
low8
 
5.9%
medium6
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

location
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
Askari
31 
Gulberg
12 
DHA Defence
10 
Bahria Town
EME Society
Other values (23)
31 

Length

Max length36
Median length7
Mean length9.452631579
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)17.9%

Sample

1st rowModel Town
2nd rowMultan Road
3rd rowEden
4th rowGulberg
5th rowEME Society

Common Values

ValueCountFrequency (%)
Askari31
32.6%
Gulberg12
 
12.6%
DHA Defence10
 
10.5%
Bahria Town6
 
6.3%
EME Society5
 
5.3%
Allama Iqbal Town3
 
3.2%
Upper Mall3
 
3.2%
Paragon City2
 
2.1%
Model Town2
 
2.1%
Eden2
 
2.1%
Other values (18)19
20.0%

Length

2022-11-16T19:31:17.773040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
askari31
20.8%
town14
 
9.4%
gulberg12
 
8.1%
dha10
 
6.7%
defence10
 
6.7%
society8
 
5.4%
bahria6
 
4.0%
eme5
 
3.4%
housing3
 
2.0%
allama3
 
2.0%
Other values (32)47
31.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

city
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
Lahore
95 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLahore
2nd rowLahore
3rd rowLahore
4th rowLahore
5th rowLahore

Common Values

ValueCountFrequency (%)
Lahore95
100.0%

Length

2022-11-16T19:31:17.902542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-16T19:31:17.977021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
lahore95
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

province_name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
Punjab
95 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPunjab
2nd rowPunjab
3rd rowPunjab
4th rowPunjab
5th rowPunjab

Common Values

ValueCountFrequency (%)
Punjab95
100.0%

Length

2022-11-16T19:31:18.044343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-16T19:31:18.121002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
punjab95
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

locality
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
Askari, Lahore, Punjab
31 
Gulberg, Lahore, Punjab
12 
DHA Defence, Lahore, Punjab
10 
Bahria Town, Lahore, Punjab
EME Society, Lahore, Punjab
Other values (23)
31 

Length

Max length52
Median length23
Mean length25.45263158
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)17.9%

Sample

1st rowModel Town, Lahore, Punjab
2nd rowMultan Road, Lahore, Punjab
3rd rowEden, Lahore, Punjab
4th rowGulberg, Lahore, Punjab
5th rowEME Society, Lahore, Punjab

Common Values

ValueCountFrequency (%)
Askari, Lahore, Punjab31
32.6%
Gulberg, Lahore, Punjab12
 
12.6%
DHA Defence, Lahore, Punjab10
 
10.5%
Bahria Town, Lahore, Punjab6
 
6.3%
EME Society, Lahore, Punjab5
 
5.3%
Allama Iqbal Town, Lahore, Punjab3
 
3.2%
Upper Mall, Lahore, Punjab3
 
3.2%
Paragon City, Lahore, Punjab2
 
2.1%
Model Town, Lahore, Punjab2
 
2.1%
Eden, Lahore, Punjab2
 
2.1%
Other values (18)19
20.0%

Length

2022-11-16T19:31:18.204315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
punjab95
28.0%
lahore95
28.0%
askari31
 
9.1%
town14
 
4.1%
gulberg12
 
3.5%
dha10
 
2.9%
defence10
 
2.9%
society8
 
2.4%
bahria6
 
1.8%
eme5
 
1.5%
Other values (34)53
15.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.50144782
Minimum31.37108
Maximum31.71727872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:18.356810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31.37108
5-th percentile31.3795184
Q131.467691
median31.522361
Q331.536068
95-th percentile31.5562024
Maximum31.71727872
Range0.34619872
Interquartile range (IQR)0.068377

Descriptive statistics

Standard deviation0.05721863647
Coefficient of variation (CV)0.001816381164
Kurtosis1.596067507
Mean31.50144782
Median Absolute Deviation (MAD)0.019753
Skewness-0.2242689936
Sum2992.637543
Variance0.003273972359
MonotonicityNot monotonic
2022-11-16T19:31:18.524621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.5331618
 
8.4%
31.5374586
 
6.3%
31.4691555
 
5.3%
31.533275
 
5.3%
31.5360684
 
4.2%
31.5372393
 
3.2%
31.3741953
 
3.2%
31.5338493
 
3.2%
31.5421143
 
3.2%
31.5183372
 
2.1%
Other values (47)53
55.8%
ValueCountFrequency (%)
31.371081
 
1.1%
31.3741953
3.2%
31.3744141
 
1.1%
31.3817061
 
1.1%
31.4000961
 
1.1%
31.4025131
 
1.1%
31.405371
 
1.1%
31.4315931
 
1.1%
31.4346681
 
1.1%
31.4377442
2.1%
ValueCountFrequency (%)
31.717278721
 
1.1%
31.5972341
 
1.1%
31.5902341
 
1.1%
31.574430551
 
1.1%
31.5679121
 
1.1%
31.5511841
 
1.1%
31.549135211
 
1.1%
31.5434311
 
1.1%
31.5421143
3.2%
31.5392061
 
1.1%

longitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.3579356
Minimum74.177749
Maximum74.474513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:18.837981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74.177749
5-th percentile74.191482
Q174.30294
median74.385309
Q374.419481
95-th percentile74.470553
Maximum74.474513
Range0.296764
Interquartile range (IQR)0.116541

Descriptive statistics

Standard deviation0.08313027288
Coefficient of variation (CV)0.00111797446
Kurtosis-0.5216629358
Mean74.3579356
Median Absolute Deviation (MAD)0.039761
Skewness-0.7380302633
Sum7064.003882
Variance0.006910642268
MonotonicityNot monotonic
2022-11-16T19:31:19.007697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.4194818
 
8.4%
74.4133236
 
6.3%
74.4705535
 
5.3%
74.413585
 
5.3%
74.4086664
 
4.2%
74.4202113
 
3.2%
74.1914823
 
3.2%
74.4096333
 
3.2%
74.3558983
 
3.2%
74.3482022
 
2.1%
Other values (48)53
55.8%
ValueCountFrequency (%)
74.1777491
 
1.1%
74.179981
 
1.1%
74.1903161
 
1.1%
74.1914823
3.2%
74.1952941
 
1.1%
74.2096852
2.1%
74.213492
2.1%
74.2140051
 
1.1%
74.2246481
 
1.1%
74.2396831
 
1.1%
ValueCountFrequency (%)
74.4745131
 
1.1%
74.4705535
5.3%
74.4561841
 
1.1%
74.455991911
 
1.1%
74.4513421
 
1.1%
74.4459061
 
1.1%
74.443696331
 
1.1%
74.4400121
 
1.1%
74.4295691
 
1.1%
74.4271371
 
1.1%

baths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.326315789
Minimum0
Maximum8
Zeros16
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:19.150524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median5
Q36
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.308366399
Coefficient of variation (CV)0.5335640095
Kurtosis-0.3551702138
Mean4.326315789
Median Absolute Deviation (MAD)1
Skewness-0.8328073281
Sum411
Variance5.328555431
MonotonicityNot monotonic
2022-11-16T19:31:19.276354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
527
28.4%
622
23.2%
016
16.8%
411
11.6%
78
 
8.4%
34
 
4.2%
24
 
4.2%
83
 
3.2%
ValueCountFrequency (%)
016
16.8%
24
 
4.2%
34
 
4.2%
411
11.6%
527
28.4%
622
23.2%
78
 
8.4%
83
 
3.2%
ValueCountFrequency (%)
83
 
3.2%
78
 
8.4%
622
23.2%
527
28.4%
411
11.6%
34
 
4.2%
24
 
4.2%
016
16.8%

area
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
10 Marla
33 
1 Kanal
21 
2 Kanal
3 Marla
 
3
12 Marla
 
3
Other values (24)
30 

Length

Max length9
Median length8
Mean length7.673684211
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)20.0%

Sample

1st row6 Kanal
2nd row1 Kanal
3rd row9 Marla
4th row1 Kanal
5th row1 Kanal

Common Values

ValueCountFrequency (%)
10 Marla33
34.7%
1 Kanal21
22.1%
2 Kanal5
 
5.3%
3 Marla3
 
3.2%
12 Marla3
 
3.2%
17 Marla3
 
3.2%
18 Marla2
 
2.1%
2 Marla2
 
2.1%
1.1 Kanal2
 
2.1%
8 Kanal2
 
2.1%
Other values (19)19
20.0%

Length

2022-11-16T19:31:19.504155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marla59
31.1%
kanal36
18.9%
1034
17.9%
121
 
11.1%
27
 
3.7%
33
 
1.6%
123
 
1.6%
173
 
1.6%
83
 
1.6%
62
 
1.1%
Other values (16)19
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

area_marla
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.10947368
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:19.659134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median10
Q320
95-th percentile101.8
Maximum200
Range198
Interquartile range (IQR)10

Descriptive statistics

Standard deviation34.37427624
Coefficient of variation (CV)1.487453877
Kurtosis12.65067532
Mean23.10947368
Median Absolute Deviation (MAD)6
Skewness3.527115082
Sum2195.4
Variance1181.590867
MonotonicityNot monotonic
2022-11-16T19:31:19.800111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1033
34.7%
2021
22.1%
405
 
5.3%
33
 
3.2%
123
 
3.2%
173
 
3.2%
182
 
2.1%
22
 
2.1%
222
 
2.1%
1602
 
2.1%
Other values (19)19
20.0%
ValueCountFrequency (%)
22
2.1%
2.51
 
1.1%
33
3.2%
41
 
1.1%
51
 
1.1%
5.51
 
1.1%
61
 
1.1%
71
 
1.1%
7.51
 
1.1%
81
 
1.1%
ValueCountFrequency (%)
2001
 
1.1%
1602
 
2.1%
1301
 
1.1%
1201
 
1.1%
941
 
1.1%
801
 
1.1%
405
 
5.3%
241
 
1.1%
222
 
2.1%
2021
22.1%

area_sqft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6291.577053
Minimum544.5
Maximum54450.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:19.951349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum544.5
5-th percentile816.75
Q12722.51
median2722.51
Q35445.02
95-th percentile27715.149
Maximum54450.2
Range53905.7
Interquartile range (IQR)2722.51

Descriptive statistics

Standard deviation9358.431098
Coefficient of variation (CV)1.487453944
Kurtosis12.65067546
Mean6291.577053
Median Absolute Deviation (MAD)1633.51
Skewness3.527115069
Sum597699.82
Variance87580232.62
MonotonicityNot monotonic
2022-11-16T19:31:20.098686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2722.5133
34.7%
5445.0221
22.1%
10890.045
 
5.3%
816.753
 
3.2%
3267.013
 
3.2%
4628.273
 
3.2%
4900.522
 
2.1%
544.52
 
2.1%
5989.522
 
2.1%
43560.162
 
2.1%
Other values (19)19
20.0%
ValueCountFrequency (%)
544.52
2.1%
680.631
 
1.1%
816.753
3.2%
10891
 
1.1%
1361.251
 
1.1%
1497.381
 
1.1%
1633.511
 
1.1%
1905.761
 
1.1%
2041.881
 
1.1%
2178.011
 
1.1%
ValueCountFrequency (%)
54450.21
 
1.1%
43560.162
 
2.1%
35392.631
 
1.1%
32670.121
 
1.1%
25591.591
 
1.1%
21780.081
 
1.1%
10890.045
 
5.3%
6534.021
 
1.1%
5989.522
 
2.1%
5445.0221
22.1%

purpose
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
For Sale
95 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFor Sale
2nd rowFor Sale
3rd rowFor Sale
4th rowFor Sale
5th rowFor Sale

Common Values

ValueCountFrequency (%)
For Sale95
100.0%

Length

2022-11-16T19:31:20.231633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-16T19:31:20.308303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
for95
50.0%
sale95
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.926315789
Minimum0
Maximum8
Zeros12
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:20.374788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.823266331
Coefficient of variation (CV)0.4643707812
Kurtosis0.6562660982
Mean3.926315789
Median Absolute Deviation (MAD)1
Skewness-0.8998456325
Sum373
Variance3.324300112
MonotonicityNot monotonic
2022-11-16T19:31:20.481274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
531
32.6%
428
29.5%
012
 
12.6%
310
 
10.5%
69
 
9.5%
23
 
3.2%
82
 
2.1%
ValueCountFrequency (%)
012
 
12.6%
23
 
3.2%
310
 
10.5%
428
29.5%
531
32.6%
69
 
9.5%
82
 
2.1%
ValueCountFrequency (%)
82
 
2.1%
69
 
9.5%
531
32.6%
428
29.5%
310
 
10.5%
23
 
3.2%
012
 
12.6%

date_added
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size888.0 B
06-18-2019
32 
07/03/2019
10 
05/03/2019
10 
04/04/2019
06-25-2019
Other values (17)
29 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)11.6%

Sample

1st row07-17-2019
2nd row10/06/2018
3rd row07/03/2019
4th row06/02/2019
5th row07/03/2019

Common Values

ValueCountFrequency (%)
06-18-201932
33.7%
07/03/201910
 
10.5%
05/03/201910
 
10.5%
04/04/20197
 
7.4%
06-25-20197
 
7.4%
04/03/20196
 
6.3%
06/11/20193
 
3.2%
02/03/20193
 
3.2%
12/05/20182
 
2.1%
06/02/20192
 
2.1%
Other values (12)13
13.7%

Length

2022-11-16T19:31:20.604929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06-18-201932
33.7%
05/03/201910
 
10.5%
07/03/201910
 
10.5%
04/04/20197
 
7.4%
06-25-20197
 
7.4%
04/03/20196
 
6.3%
06/11/20193
 
3.2%
02/03/20193
 
3.2%
12/05/20182
 
2.1%
06/02/20192
 
2.1%
Other values (12)13
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.957895
Minimum2018
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:20.724940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2019
Q12019
median2019
Q32019
95-th percentile2019
Maximum2019
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2018947205
Coefficient of variation (CV)9.999947053 × 10-5
Kurtosis19.8878751
Mean2018.957895
Median Absolute Deviation (MAD)0
Skewness-4.633523198
Sum191801
Variance0.04076147816
MonotonicityNot monotonic
2022-11-16T19:31:20.830195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
201991
95.8%
20184
 
4.2%
ValueCountFrequency (%)
20184
 
4.2%
201991
95.8%
ValueCountFrequency (%)
201991
95.8%
20184
 
4.2%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.673684211
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:20.944811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q36
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.789104764
Coefficient of variation (CV)0.3153338638
Kurtosis3.805228521
Mean5.673684211
Median Absolute Deviation (MAD)0
Skewness0.5405337936
Sum539
Variance3.200895857
MonotonicityNot monotonic
2022-11-16T19:31:21.053468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
648
50.5%
713
 
13.7%
413
 
13.7%
510
 
10.5%
24
 
4.2%
12
 
2.1%
122
 
2.1%
101
 
1.1%
31
 
1.1%
111
 
1.1%
ValueCountFrequency (%)
12
 
2.1%
24
 
4.2%
31
 
1.1%
413
 
13.7%
510
 
10.5%
648
50.5%
713
 
13.7%
101
 
1.1%
111
 
1.1%
122
 
2.1%
ValueCountFrequency (%)
122
 
2.1%
111
 
1.1%
101
 
1.1%
713
 
13.7%
648
50.5%
510
 
10.5%
413
 
13.7%
31
 
1.1%
24
 
4.2%
12
 
2.1%

day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.37894737
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-11-16T19:31:21.169825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median11
Q318
95-th percentile25
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.435208567
Coefficient of variation (CV)0.7412995503
Kurtosis-1.315944037
Mean11.37894737
Median Absolute Deviation (MAD)7
Skewness0.3412138252
Sum1081
Variance71.15274356
MonotonicityNot monotonic
2022-11-16T19:31:21.289484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1833
34.7%
329
30.5%
49
 
9.5%
257
 
7.4%
54
 
4.2%
113
 
3.2%
22
 
2.1%
302
 
2.1%
171
 
1.1%
61
 
1.1%
Other values (4)4
 
4.2%
ValueCountFrequency (%)
11
 
1.1%
22
 
2.1%
329
30.5%
49
 
9.5%
54
 
4.2%
61
 
1.1%
101
 
1.1%
113
 
3.2%
121
 
1.1%
171
 
1.1%
ValueCountFrequency (%)
302
 
2.1%
261
 
1.1%
257
 
7.4%
1833
34.7%
171
 
1.1%
121
 
1.1%
113
 
3.2%
101
 
1.1%
61
 
1.1%
54
 
4.2%

Interactions

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2022-11-16T19:30:59.594187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-16T19:31:05.099801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-16T19:31:06.783449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-16T19:31:08.254946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-16T19:31:09.947689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-16T19:31:11.775657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-16T19:31:13.484455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-16T19:31:21.413944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-16T19:31:21.780103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-16T19:31:21.990955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-16T19:31:22.202350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-16T19:31:22.406740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-16T19:31:15.187198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-16T19:31:15.697370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthday
03477958https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.htmlHouse220000000Very HighModel TownLahorePunjabModel Town, Lahore, Punjab31.48386974.32568606 Kanal120.032670.12For Sale007-17-20192019717
148289248https://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.htmlHouse40000000Very HighMultan RoadLahorePunjabMultan Road, Lahore, Punjab31.43159374.17998051 Kanal20.05445.02For Sale510/06/20182018106
255596275https://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.htmlHouse9500000LowEdenLahorePunjabEden, Lahore, Punjab31.49934874.41695909 Marla9.02450.26For Sale307/03/2019201973
37852893102https://www.zameen.com/Property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.htmlHouse52000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.49590974.35056961 Kanal20.05445.02For Sale506/02/2019201962
49830653749https://www.zameen.com/Property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.htmlHouse32500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43997874.20968501 Kanal20.05445.02For Sale507/03/2019201973
59830663745https://www.zameen.com/Property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.htmlHouse31500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43774474.21349001 Kanal20.05445.02For Sale607/03/2019201973
612866433733https://www.zameen.com/Property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.htmlHouse13500000MediumEdenLahorePunjabEden, Lahore, Punjab31.44111374.23968347.5 Marla7.52041.88For Sale404/04/2019201944
71402784514https://www.zameen.com/Property/lahore_upper_mall_commercial_old_house_for_sale_upper_mall_lahore_excellent_location-1402784-514-1.htmlHouse87500000Very HighUpper MallLahorePunjabUpper Mall, Lahore, Punjab31.54211474.35589851.2 Kanal24.06534.02For Sale406-30-20192019630
8160671069https://www.zameen.com/Property/lahore_cavalry_ground_10_marla_house_is_available_for_sale-1606710-69-1.htmlHouse16500000HighCavalry GroundLahorePunjabCavalry Ground, Lahore, Punjab31.50055774.367730010 Marla10.02722.51For Sale404/04/2019201944
916468801781https://www.zameen.com/Property/bahria_town_sector_b_bahria_town_umar_block_double_storey_house_for_sale-1646880-1781-1.htmlHouse18500000HighBahria TownLahorePunjabBahria Town, Lahore, Punjab31.38170674.195294010 Marla10.02722.51For Sale007/04/2019201974

Last rows

property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthday
8545368543852https://www.zameen.com/Property/gulberg_main_boulevard_gulberg_commercialized_piece_of_property_house_for_sale-4536854-3852-1.htmlHouse1200000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51943574.34554808 Kanal160.043560.16For Sale004/04/2019201944
8645577993850https://www.zameen.com/Property/gulberg_mm_alam_road_shopping_paradise_of_lahore_house_for_sale-4557799-3850-1.htmlHouse380000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51047174.35052602 Kanal40.010890.04For Sale005/03/2019201953
874560911496https://www.zameen.com/Property/mozang_mozang_chungi_al_qader_center_ground_floor_new_apartment_for_sale-4560911-496-1.htmlFlat3200000LowMozangLahorePunjabMozang, Lahore, Punjab31.54913574.31511723 Marla3.0816.75For Sale207/03/2019201973
8845709881447https://www.zameen.com/Property/dha_defence_dha_phase_5_original_faisal_rasool_brand_new_classical_bungalow-4570988-1447-1.htmlHouse59800000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46249374.40934271 Kanal20.05445.02For Sale605/03/2019201953
8946771018172https://www.zameen.com/Property/dha_defence_defence_raya_dha_raya_2_kanal_facing_golf_course_fully_basement_house_for_sale-4677101-8172-1.htmlHouse66000000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46915574.47055362 Kanal40.010890.04For Sale606-25-20192019625
904747413154https://www.zameen.com/Property/gulberg_zafar_ali_road_6_kanal_house_for_sale_on_zafar_ali_road_mall_road_upper_mall_lahore_excellent_location-4747413-154-1.htmlHouse360000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.53842074.352357710 Kanal200.054450.20For Sale506-30-20192019630
914907568514https://www.zameen.com/Property/lahore_upper_mall_4_kanal__house_for_sale_upper_mall_lahore-4907568-514-1.htmlHouse160000000Very HighUpper MallLahorePunjabUpper Mall, Lahore, Punjab31.54211474.35589861 Kanal20.05445.02For Sale501-18-20192019118
9249406298https://www.zameen.com/Property/lahore_model_town_3_marla_house_for_sale-4940629-8-1.htmlHouse8000000LowModel TownLahorePunjabModel Town, Lahore, Punjab31.47388474.32908033 Marla3.0816.75For Sale207/03/2019201973
9349514517https://www.zameen.com/Property/lahore_gulberg_blue_zone_house_for_sale-4951451-7-1.htmlHouse480000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.52236174.34717204 Kanal80.021780.08For Sale005/03/2019201953
9450193101534https://www.zameen.com/Property/garden_town_garden_town_ahmed_block_purely_residential_apartments_for_sale-5019310-1534-1.htmlFlat22500000Very HighGarden TownLahorePunjabGarden Town, Lahore, Punjab31.50780074.31873307 Marla7.01905.76For Sale004/03/2019201943